



import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


A__    :    Tuple        =   logging.get_logger(__name__)  # pylint: disable=invalid-name


def      UpperCamelCase(    __UpperCamelCase				:						Optional[Any]					):
		warnings.warn(
		    '''The preprocess method is deprecated and will be removed in a future version. Please'''
		    ''' use VaeImageProcessor.preprocess instead'''	,snake_case__	,)
		if isinstance(snake_case__	,torch.Tensor					):
				return image
		elif isinstance(snake_case__	,PIL.Image.Image					):
				lowerCAmelCase_     :	Optional[Any]							   =					[image]

		if isinstance(image[0]	,PIL.Image.Image					):
				lowerCAmelCase_   ,    lowerCAmelCase_     :	Union[str, Any]							   =					image[0].size
				lowerCAmelCase_   ,    lowerCAmelCase_     :	Any							   =					(x - x % 8 for x in (w, h))  # resize to integer multiple of 8

				lowerCAmelCase_     :	Tuple							   =					[np.array(i.resize((w, h)	,resample=PIL_INTERPOLATION['''lanczos''']					)					)[None, :] for i in image]
				lowerCAmelCase_     :	Optional[Any]							   =					np.concatenate(snake_case__	,axis=0					)
				lowerCAmelCase_     :	int							   =					np.array(snake_case__					).astype(np.floataa					) / 2_5_5.0
				lowerCAmelCase_     :	Union[str, Any]							   =					image.transpose(0	,3	,1	,2					)
				lowerCAmelCase_     :	Optional[int]							   =					2.0 * image - 1.0
				lowerCAmelCase_     :	Any							   =					torch.from_numpy(snake_case__					)
		elif isinstance(image[0]	,torch.Tensor					):
				lowerCAmelCase_     :	List[str]							   =					torch.cat(snake_case__	,dim=0					)
		return image


def      UpperCamelCase(    __UpperCamelCase				:						Tuple					):
		if isinstance(snake_case__	,torch.Tensor					):
				return mask
		elif isinstance(snake_case__	,PIL.Image.Image					):
				lowerCAmelCase_     :	Tuple							   =					[mask]

		if isinstance(mask[0]	,PIL.Image.Image					):
				lowerCAmelCase_   ,    lowerCAmelCase_     :	Dict							   =					mask[0].size
				lowerCAmelCase_   ,    lowerCAmelCase_     :	Optional[Any]							   =					(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
				lowerCAmelCase_     :	Optional[Any]							   =					[np.array(m.convert('''L'''					).resize((w, h)	,resample=PIL_INTERPOLATION['''nearest''']					)					)[None, :] for m in mask]
				lowerCAmelCase_     :	int							   =					np.concatenate(snake_case__	,axis=0					)
				lowerCAmelCase_     :	Optional[int]							   =					mask.astype(np.floataa					) / 2_5_5.0
				lowerCAmelCase_     :	str							   =					0
				lowerCAmelCase_     :	Tuple							   =					1
				lowerCAmelCase_     :	Optional[Any]							   =					torch.from_numpy(snake_case__					)
		elif isinstance(mask[0]	,torch.Tensor					):
				lowerCAmelCase_     :	Union[str, Any]							   =					torch.cat(snake_case__	,dim=0					)
		return mask





class 							__snake_case		(	UpperCAmelCase__		):
							_a      =					42
							_a      =					42




							def __init__(			self	:			int							,							A_	:			int							,							A_	:			List[Any]):
									super().__init__()
									self.register_modules(unet=_a							,							scheduler=_a)







							@torch.no_grad()
							def __call__(			self	:			List[str]							,							A_	:			Tuple							,							A_	:			List[str]							,							A_	:			str = 2_5_0							,							A_	:			int = 0.0							,							A_	:			List[str] = 1_0							,							A_	:			List[str] = 1_0							,							A_	:			int = None							,							A_	:			Optional[int] = "pil"							,							A_	:			Dict = True							,							):
									lowerCAmelCase_     :	Dict							   =					image

									lowerCAmelCase_     :	List[Any]							   =					_preprocess_image(_a)
									lowerCAmelCase_     :	Dict							   =					original_image.to(device=self.device							,							dtype=self.unet.dtype)
									lowerCAmelCase_     :	int							   =					_preprocess_mask(_a)
									lowerCAmelCase_     :	List[Any]							   =					mask_image.to(device=self.device							,							dtype=self.unet.dtype)

									lowerCAmelCase_     :	Any							   =					original_image.shape[0]

									# sample gaussian noise to begin the loop
									if isinstance(_a							,							_a) and len(_a) != batch_size:
											raise ValueError(
											    F"""You have passed a list of generators of length {len(_a)}, but requested an effective batch"""
											    F""" size of {batch_size}. Make sure the batch size matches the length of the generators.""")

									lowerCAmelCase_     :	Dict							   =					original_image.shape
									lowerCAmelCase_     :	Dict							   =					randn_tensor(_a							,							generator=_a							,							device=self.device							,							dtype=self.unet.dtype)

									# set step values
									self.scheduler.set_timesteps(_a							,							_a							,							_a							,							self.device)
									lowerCAmelCase_     :	List[str]							   =					eta

									lowerCAmelCase_     :	Dict							   =					self.scheduler.timesteps[0] + 1
									lowerCAmelCase_     :	Tuple							   =					generator[0] if isinstance(_a							,							_a) else generator
									for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
											if t < t_last:
													# predict the noise residual
													lowerCAmelCase_     :	Any							   =					self.unet(_a							,							_a).sample
													# compute previous image: x_t -> x_t-1
													lowerCAmelCase_     :	str							   =					self.scheduler.step(_a							,							_a							,							_a							,							_a							,							_a							,							_a).prev_sample

											else:
													# compute the reverse: x_t-1 -> x_t
													lowerCAmelCase_     :	Any							   =					self.scheduler.undo_step(_a							,							_a							,							_a)
											lowerCAmelCase_     :	Any							   =					t

									lowerCAmelCase_     :	List[str]							   =					(image / 2 + 0.5).clamp(0							,							1)
									lowerCAmelCase_     :	Dict							   =					image.cpu().permute(0							,							2							,							3							,							1).numpy()
									if output_type == "pil":
											lowerCAmelCase_     :	Union[str, Any]							   =					self.numpy_to_pil(_a)

									if not return_dict:
											return (image,)

									return ImagePipelineOutput(images=_a)











"""simple docstring"""




import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class        __magic_name__				(			UpperCAmelCase__		,   unittest.TestCase							):






				'''simple docstring'''
				__UpperCamelCase         =		DiTPipeline
				__UpperCamelCase         =		CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
				__UpperCamelCase         =		PipelineTesterMixin.required_optional_params - {
				    "latents",
				    "num_images_per_prompt",
				    "callback",
				    "callback_steps",
				}
				__UpperCamelCase         =		CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
				__UpperCamelCase         =		False





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							torch.manual_seed(0					)
							lowerCamelCase      =   TransformeraDModel(
							    sample_size=16						,					num_layers=2						,					patch_size=4						,					attention_head_dim=8						,					num_attention_heads=2						,					in_channels=4						,					out_channels=8						,					attention_bias=_a						,					activation_fn="""gelu-approximate"""						,					num_embeds_ada_norm=1_000						,					norm_type="""ada_norm_zero"""						,					norm_elementwise_affine=_a						,					)
							lowerCamelCase      =   AutoencoderKL()
							lowerCamelCase      =   DDIMScheduler()
							lowerCamelCase      =   {"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
							return components





				def   _lowerCAmelCase							(			self						,					_a						,					_a=0					):





							"""simple docstring"""
							if str(_a					).startswith("""mps"""					):
										lowerCamelCase      =   torch.manual_seed(_a					)
							else:
										lowerCamelCase      =   torch.Generator(device=_a					).manual_seed(_a					)
							lowerCamelCase      =   {
							    """class_labels""": [1],
							    """generator""": generator,
							    """num_inference_steps""": 2,
							    """output_type""": """numpy""",
							}
							return inputs





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							lowerCamelCase      =   """cpu"""

							lowerCamelCase      =   self.get_dummy_components()
							lowerCamelCase      =   self.pipeline_class(**_a					)
							pipe.to(_a					)
							pipe.set_progress_bar_config(disable=_a					)

							lowerCamelCase      =   self.get_dummy_inputs(_a					)
							lowerCamelCase      =   pipe(**_a					).images
							lowerCamelCase      =   image[0, -3:, -3:, -1]

							self.assertEqual(image.shape						,					(1, 16, 16, 3)					)
							lowerCamelCase      =   np.array([0.2_946, 0.6_601, 0.4_329, 0.3_296, 0.4_144, 0.5_319, 0.7_273, 0.5_013, 0.4_457]					)
							lowerCamelCase      =   np.abs(image_slice.flatten() - expected_slice					).max()
							self.assertLessEqual(_a						,					1e-3					)





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							self._test_inference_batch_single_identical(relax_max_difference=_a						,					expected_max_diff=1e-3					)





				@unittest.skipIf(
				    torch_device != """cuda""" or not is_xformers_available()						,					reason="""XFormers attention is only available with CUDA and `xformers` installed"""						,					)
				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3					)






@require_torch_gpu
@slow
class        __magic_name__				(			unittest.TestCase							):






				'''simple docstring'''





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							lowerCamelCase      =   torch.manual_seed(0					)

							lowerCamelCase      =   DiTPipeline.from_pretrained("""facebook/DiT-XL-2-256"""					)
							pipe.to("""cuda"""					)

							lowerCamelCase      =   ["""vase""", """umbrella""", """white shark""", """white wolf"""]
							lowerCamelCase      =   pipe.get_label_ids(_a					)

							lowerCamelCase      =   pipe(_a						,					generator=_a						,					num_inference_steps=40						,					output_type="""np"""					).images

							for word, image in zip(_a						,					_a					):
										lowerCamelCase      =   load_numpy(
										    f'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy'					)
										assert np.abs((expected_image - image).max()					) < 1e-2





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							lowerCamelCase      =   DiTPipeline.from_pretrained("""facebook/DiT-XL-2-512"""					)
							lowerCamelCase      =   DPMSolverMultistepScheduler.from_config(pipe.scheduler.config					)
							pipe.to("""cuda"""					)

							lowerCamelCase      =   ["""vase""", """umbrella"""]
							lowerCamelCase      =   pipe.get_label_ids(_a					)

							lowerCamelCase      =   torch.manual_seed(0					)
							lowerCamelCase      =   pipe(_a						,					generator=_a						,					num_inference_steps=25						,					output_type="""np"""					).images

							for word, image in zip(_a						,					_a					):
										lowerCamelCase      =   load_numpy(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
										    f'/dit/{word}_512.npy'					)

										assert np.abs((expected_image - image).max()					) < 1e-1



